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25-10-2017 | Original article | Issue 6/2017 Open Access

Chinese Journal of Mechanical Engineering 6/2017

Adaptive Change Detection for Long-Term Machinery Monitoring Using Incremental Sliding-Window

Chinese Journal of Mechanical Engineering > Issue 6/2017
Teng Wang, Guo-Liang Lu, Jie Liu, Peng Yan
Important notes
Supported by National Natural Science Foundation of China (Grant Nos. 61403232, 61327003), Shandong Provincial Natural Science Foundation of China (Grant No. ZR2014FQ025), and Young Scholars Program of Shandong University, China (YSPSDU, 2015WLJH30).


Detection of structural changes from an operational process is a major goal in machine condition monitoring. Existing methods for this purpose are mainly based on retrospective analysis, resulting in a large detection delay that limits their usages in real applications. This paper presents a new adaptive real-time change detection algorithm, an extension of the recent research by combining with an incremental sliding-window strategy, to handle the multi-change detection in long-term monitoring of machine operations. In particular, in the framework, Hilbert space embedding of distribution is used to map the original data into the Re-producing Kernel Hilbert Space (RKHS) for change detection; then, a new adaptive threshold strategy can be developed when making change decision, in which a global factor (used to control the coarse-to-fine level of detection) is introduced to replace the fixed value of threshold. Through experiments on a range of real testing data which was collected from an experimental rotating machinery system, the excellent detection performances of the algorithm for engineering applications were demonstrated. Compared with state-of-the-art methods, the proposed algorithm can be more suitable for long-term machinery condition monitoring without any manual re-calibration, thus is promising in modern industries.

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